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FP-IND-PREDMNT1 is an STM32Cube function pack including dedicated algorithms for advanced time and frequency domain signal processing and analysis of 3D digital accelerometers with flat bandwidth up to 5 kHz.
The package includes pressure, relative humidity and temperature sensor monitoring, and audio algorithms for acoustic emission (AE), up to 20 kHz, and ultrasound emission analysis up to 80 kHz.
According to the designer's needs for connectivity and sensors, different options are available and selectable by changing parameter settings.
With the NUCLEO-F446RE development board and the STEVAL-STWINKT1 kit with BLE connectivity, you can monitor and log the algorithm output and sensor data using the STBLESensor app.
The STEVAL-IDP005V1 included in the STEVAL-BFA001V1B evaluation kit allows connecting a PC via USB to monitor and log the algorithm output, sensor data and equipment status.
By using the STEVAL-STWINKT1 kit with Wi-Fi connectivity, you can connect the device to the dedicated DSH-PREDMNT web-based dashboard to monitor and log the algorithm output, sensor data and equipment status.
The FP-IND-PREDMNT1, together with the suggested combination of STM32 and ST devices, can be used to develop specific industrial predictive maintenance applications for early detection of warning signs of potential failure.
The software runs on the STM32 microcontroller and includes all the necessary drivers for the STM32 Nucleo development board and expansion boards, as well as for the STEVAL-IDP005V1 and STEVAL-STWINKT1 evaluation kits.主要特性
- Complete firmware to develop a sensor node for condition monitoring and predictive maintenance applications, featuring digital or analog microphone, environmental and motion sensors, and performing real-time monitoring of parameters and equipment status via:
- user terminal by UART (available for the STEVAL-IDP005V1 evaluation board included in the STEVAL-BFA001V1B kit)
- Wi-Fi connectivity (available for the STEVAL-STWINKT1 with the STEVAL-STWINWFV1 Wi-Fi board)
- BLE connectivity (available for the NUCLEO-F446RE development board and the STEVAL-STWINKT1 kit)
- Signal processing (MotionSP) middleware for vibration analysis in time domain (speed RMS and acceleration peak) and frequency domain (FFT with programmable size, averaging, overlapping and windowing)
- PDM to PCM and sound pressure level (SPL) middleware for digital microphones (for NUCLEO-F446RE and STEVAL-BFA001V1B)
- Acoustic FFT Analysis for NUCLEO-F446RE and STEVAL-BFA001V1B
- Ultrasound FFT Analysis for STEVAL-STWINKT1
- Configurable alarm and warning thresholds for key parameters
- Data logging capability through mobile app or PC serial terminal
- Compatible with STBLESensor application for Android/iOS, to perform sensor data reading, audio and motion algorithm feature demo, and firmware update over the air (FOTA) (feature available only when using NUCLEO-F446RE and STEVAL-STWINKT1 with BLE connectivity)
- Compatible with DSH-PREDMNT web-based predictive maintenance dashboard for monitoring sensor data and device status (feature available only when using the STEVAL-STWINKT1 with the STEVAL-STWINWFV1 Wi-Fi adapter)
- Sample implementation available for the STEVAL-BFA001V1B and STEVAL-STWINKT1 kits
- Sample implementation available for X-NUCLEO-CCA02M2, X-NUCLEO-IKS01A3 or X-NUCLEO-IKS01A2, STEVAL-MKI182V2 (DIL24 based on ISM330DLC) and X-NUCLEO-BNRG2A1 connected to a NUCLEO-F446RE board
- Easy portability across different MCU families, thanks to STM32Cube
- Free, user-friendly license terms
- Complete firmware to develop a sensor node for condition monitoring and predictive maintenance applications, featuring digital or analog microphone, environmental and motion sensors, and performing real-time monitoring of parameters and equipment status via:
获取软件
| 产品型号 | 一般描述 | Software Version | 供应商 | 下载 |
|---|---|---|---|---|
| FP-IND-PREDMNT1 | STM32Cube function pack for multi sensors node with signal processing to enable predictive maintenance | 2.2.0 | ST |
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产品规格 (1)
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| 21 May 2020 |
21 May 2020
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用户手册 (1)
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| 28 Jul 2020 |
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| 11 Nov 2020 |
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